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Posts Tagged ‘Price-to-book Value’

The Fall 2010 edition of the Graham and Doddsville Newsletter, Columbia Business School‘s student-led investment newsletter co-sponsored by the Heilbrunn Center for Graham & Dodd Investing and the Columbia Investment Management Association, has a fascinating interview with Donald G. Smith. Smith, who volunteered for Benjamin Graham at UCLA, concentrates on the bottom decile of price to tangible book stocks and has compounded at 15.3% over 30 years:

G&D: Briefly describe the history of your firm and how you got started?

DS: Donald Smith & Co. was founded in 1980 and now has $3.6 billion under management. Over 30 years since inception our compounded annualized return is 15.3%. Over the last 10 years our annualized return is 12.1% versus −0.4% for the S&P 500.

Our investment philosophy goes back to when I was going to UCLA Law School and Benjamin Graham was teaching in the UCLA Business School. In one of his lectures he discussed a Drexel Firestone study which analyzed the performance of a portfolio of the lowest P/E third of the Dow Jones (which was the beginning of ―Dogs of the Dow 30). Graham wanted to update that study but he didn‘t have access to a database in those days, so he asked for volunteers to manually calculate the data. I was curious about this whole approach so I decided to volunteer. There was no question that this approach beat the market. However, doing the analysis, especially by hand, you could see some of the flaws in the P/E based approach. Based on the system you would buy Chrysler every time the earnings boomed and it was selling at only a 5x P/E, but the next year or two they would go into a down cycle, the P/E would expand and you were forced to sell it. So in effect, you were often buying high and selling low. So it dawned on me that P/E and earnings were too volatile to base an investment philosophy on. That‘s why I started playing with book value to develop a better investment approach based on a more stable metric.

G&D: There are plenty of studies suggesting that the lowest price to book stocks outperform. However, only 1/10 of 1% of all money managers focus on the lowest decile of price to book stocks. Why do you think that‘s so, and how do people ignore all of this evidence?

DS: They haven‘t totally ignored it. There are periods of time when quant funds, in particular, use this strategy. However a lot of the purely quant funds buying low price to book stocks have blown up, as was the case in the summer of 2007. Now not as many funds are using the approach. Low price to book stocks tend to be out-of-favor companies. Often their earnings are really depressed, and when earnings are going down and stock prices are going down, it‘s a tough sell.

G&D: Would you mind talking about how the composition of that bottom decile has changed over time? Is it typically composed of firms in particular out of favor industries or companies dealing with specific issues unique to them?

DS: The bulk is companies with specific issues unique to them, but often there is a sector theme. Back in the early 1980‘s small stocks were all the rage and big slow-growing companies were very depressed. At that time we loaded up on a lot of these large companies. Then the KKR‘s of the world started buying them because of their stable cash flow and the stocks went up. About six years ago, a lot of the energy-related stocks were very cheap. We owned oil shipping, oil services and coal companies trading below book and liquidation value. When oil went up they became the darlings of Wall Street. Over the years we have consistently owned electric utilities because there always seem to be stocks that are temporarily depressed because of a bad rate decision by the public service commission. Also, cyclicals have been a staple for us over the years because, by definition, they go up and down a lot which gives us buying opportunities. We‘ve been in and out of the hotel group, homebuilders, airlines, and tech stocks.

Performance of the low-price-to-tangible book value:

Read the Graham and Doddsville newsletter Fall 2010 (.pdf).

Hat tip George.

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In a post in late November last year, Testing the performance of price-to-book value, I set up a hypothetical equally-weighted portfolio of the cheapest price-to-book stocks with a positive P/E ratio discovered using the Google Screener, which I called the “Greenbackd Contrarian Value Portfolio“. The portfolio has been operating for a little over 4 months, so I thought I’d check in and see how it’s going.

Here is the Tickerspy portfolio tracker for the Greenbackd Contrarian Value Portfolio showing how each individual stock is performing:

(Click to enlarge)

And the chart showing the performance of the portfolio against the S&P500:

[Full Disclosure:  No positions. This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.]

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As I’ve discussed in the past, P/B and P/E are demonstratively useful as predictors of future stock returns, and more so when combined (see, for example, LSV’s Two-Dimensional Classifications). As Josef Lakonishok, Andrei Shleifer, and Robert Vishny showed in Contrarian Investment, Extrapolation, and Risk, within the set of firms whose B/M ratios are the highest (in other words, the lowest price-to-book value), further sorting on the basis of another value variable – whether it be C/P, E/P or low GS – enhances returns. In that paper, LSV concluded that value strategies based jointly on past performance and expected future performance produce higher returns than “more ad hoc strategies such as that based exclusively on the B/M ratio.” A new paper further discusses the relationship between E/P and B/P from an accounting perspective, and the degree to which E/P and B/P together predict stock returns.

The CXO Advisory Group Blog, fast becoming one of my favorite sites for new investment research, has a new post, Combining E/P and B/P, on a December 2009 paper titled “Returns to Buying Earnings and Book Value: Accounting for Growth and Risk” by Francesco Reggiani and Stephen Penman. Penman and Reggiani looked at the relationship between E/P and B/P from an accounting perspective:

This paper brings an accounting perspective to the issue: earnings and book values are accounting numbers so, if the two ratios indicate risk and return, it might have something to do with accounting principles for measuring earnings and book value.

Indeed, an accounting principle connects earnings and book value to risk: under uncertainty, accounting defers the recognition of earnings until the uncertainty has largely been resolved. The deferral of earnings to the future reduces book value, reduces short-term earnings relative to book value, and increases expected long-term earnings growth.

CXO summarize the authors’ methodology and findings as follows:

Using monthly stock return and firm financial data for a broad sample of U.S. stocks spanning 1963-2006 (153,858 firm-years over 44 years), they find that:

  • E/P predicts stock returns, consistent with the idea that it measures risk to short-term earnings.
  • B/P predicts stock returns, consistent with the idea that it measures accounting deferral of risky earnings and therefore risk to both short-term and long-term earnings. This perspective disrupts the traditional value-growth paradigm by associating expected earnings growth with high B/P.
  • For a given E/P, B/P therefore predicts incremental return associated with expected earnings growth. A joint sort on E/P and B/P discovers this incremental return and therefore generates higher returns than a sort on E/P alone, attributable to additional risk (see the chart below).
  • Results are somewhat stronger for the 1963-1984 subperiod than for the 1985-2006 subperiod.
  • Results using consensus analyst forecasts rather than lagged earnings to calculate E/P over the 1977-2006 subperiod are similar, but not as strong.

CXO set out Penman and Reggiani’s “core results” in the following table (constructed by CXO from Penman and Reggiani’s results):

The following chart, constructed from data in the paper, compares average annual returns for four sets of quintile portfolios over the entire 1963-2006 sample period, as follows:

  • “E/P” sorts on lagged earnings yield.
  • “B/P” sorts on lagged book-to-price ratio.
  • “E/P:B/P” sorts first on E/P and then sorts each E/P quintile on B/P. Reported returns are for the nth B/P quintile within the nth E/P quintile (n-n).
  • “B/P:E/P” sorts first on B/P and then sorts each B/P quintile on E/P. Reported returns are for the nth E/P quintile within the nth B/P quintile (n-n).

Start dates for return calculations are three months after fiscal year ends (when annual financial reports should be available). The holding period is 12 months. Results show that double sorts generally enhance performance discrimination among stocks. E/P measures risk to short-term earnings and therefore short-term earnings growth. B/P measures risk to short-term earnings and earnings growth and therefore incremental earnings growth. The incremental return for B/P is most striking in low E/P quintile.

The paper also discusses in some detail a phenomenon that I find deeply fascinating, mean reversion in earnings predicted by low price-to-book values:

Research (in Fama and French 1992, for example) shows that book-to-price (B/P) also predicts stock returns, so consistently so that Fama and French (1993 and 1996) have built an asset pricing model based on the observation. The same discussion of rational pricing versus market inefficiency ensues but, despite extensive modeling (and numerous conjectures), the phenomenon remains a mystery. The mystery deepens when it is said that B/P is inversely related to earnings growth while positively related to returns; low B/P stocks (referred to as “growth” stocks) yield lower returns than high B/P stocks (“value” stocks). Yet investment professionals typically think of growth as risky, requiring higher returns, consistent with the risk-return notion that one cannot buy more earnings (growth) without additional risk.

(emphasis mine)

The paper adds further weight to the predictive ability of low price-to-book value and low price-to-earnings ratios. Its conclusion that book-to-price indicates expected returns associated with expected earnings growth is particularly interesting, and accords with the same findings in Werner F.M. DeBondt and Richard H. Thaler in Further Evidence on Investor Overreaction and Stock Market Seasonality.

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I’m setting up a new experiment for 2009/2010 along the same lines as the 2008/2009 Net Net vs Activist Legend thought experiment pitting a little Graham net net against activist investing legend Carl Icahn (Net Net vs Activist Legend: And the winner is…). This time around I’m pitting a small portfolio of near Graham net nets against a small portfolio of ultra-low price-to-book value stocks. The reason? Near Graham net nets are stocks trading at a small premium to Graham’s two-thirds NCAV cut-off, but still trading at a discount to NCAV. While they are also obviously trading at a discount to book, they will in many cases trade at a higher price-to-book value ratio than a portfolio of stocks selected on the basis of price-to-book only. I’m interested to see which will perform better in 2010. The two portfolios are set out below (each contains 30 stocks). I’ll track the equal-weighted returns of each through the year.

The Near Graham Net Net Portfolio (extracted from the Graham Investor screen):

The Ultra-low Price-to-book Portfolio:

The Ultra-low Price-to-book Portfolio contains a sickly lot from a net current asset value perspective. Most have a negative net current asset value, as their liabilities exceed their current assets. Where that occurs, the proportion of price to NCAV is meaningless, so I’ve just recorded it as “N/A”. The few stocks that do have a positive net current asset value are generally trading a substantial premium to that value, with the exception of NWD and ZING, which qualify as Graham net nets.

While the Net Net vs Activist Legend thought experiment didn’t amount to (ahem) a formal academic study, there are two studies relevant to the outcome in that experiment: Professor Henry Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update, which found “[the] mean return from net current asset stocks for the 13-year period [from 1970 to 1983] was 29.4% per year versus 11.5% per year for the NYSE-AMEX Index.” Also relevant was Hedge Fund Activism, Corporate Governance, and Firm Performance, by Brav, Jiang, Thomas and Partnoy, in which the authors found that the “market reacts favorably to hedge fund activism, as the abnormal return upon announcement of potential activism is in the range of [7%] seven percent, with no return reversal during the subsequent year.”

This experiment is similar to the Net Net vs Activist Legend thought experiment in that it isn’t statistically significant. There are, however, several studies relevant to divining the outcome. In this instance, Professor Oppenheimer’s study speaks to the return on the Near Graham Net Net Portfolio, as Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986), Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987), Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s Contrarian Investment, Extrapolation and Risk (1994) as updated by The Brandes Institute’s Value vs Glamour: A Global Phenomenon (2008) speak to the return on the Ultra-low Price-to-book Portfolio. One wrinkle in that theory is that the low price-to-book value studies only examine the cheapest quintile and decile, where I have taken the cheapest 30 stocks on the Google Finance screener, which is the cheapest decile of the cheapest decile. I expect these stocks to do better than the low price-to-book studies would suggest. That said, I expect that the Near Graham Net Net Portfolio will outperform the Ultra-low Price-to-book Portfolio by a small margin. Let me know which horse you’re getting on and the reason in the comments.

[Full Disclosure:  I hold RCMT and TSRI. This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.]

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In yesterday’s post we discussed some informal analysis I’ve undertaken on the returns to the quantitative investment strategy known as “High Minus Low” or HML. The first step was an analysis of HML’s components, high and low BM stocks. I described the HML strategy in some detail and analysed the long-term diminution in the returns to those components. I think that the returns to both high BM and low BM stocks have been attenuating significantly over time. The phenomenon persisted over whichever recent period I analysed (since 1926 to the present day, or over the last 25, 20, 15 or 10 years). This suggested that it’s got harder over time to earn excess returns as a value investor employing a high BM strategy.

In today’s post, I analyse the returns to HML at the strategy level and ask whether the returns to HML are really just returns to a levered high BM strategy. In a Goldman Sachs Asset Management (GSAM) presentation, Maybe it really is different this time, GSAM argued that the returns to HML have diminished since August 2007 because too many investors are employing the same  strategy, a phenomenon GSAM describe as “overcrowding.” In summary, I agree with GSAM’s view that the returns to HML have indeed stagnated since late 2007. I’m not sure that this is attributable to “overcrowding” as GSAM suggests or just a function of the underlying market performance of the components of HML (i.e. everything has been and continues to be expensive, leaving little room for good returns). Interestingly, even accounting for the period of low attenuated performance between August 2007 and the present, the HML strategy has performed reasonably well over the last 10 years, which has been a period of diminished (or non-existent) returns for equities. The returns to a 130/30 HML strategy over the last 10 years significantly outpaced a high BM strategy and the market in general. Surprisingly (to me at least), the low BM short didn’t add much to HML returns. This is especially surprising give that the period analysed was one where the low BM stocks bore the brunt of the collapse. That observation requires some further analysis, but it’s a prima facie argument that most of the returns to HML are due to the leverage inherent in the strategy.

Returns to HML

Most hedge fund strategies being proprietary and, hence, closely guarded secrets, I’m not sure what the typical HML strategy looks like. For the sake of this argument, I’ve constructed three HML portfolios. The first is 30% short the low BM decile and 130% long the high BM decile, which is a not uncommon hedge fund strategy. The second is 100% short the low BM decile and 100% long the high BM decile, which highlights the low BM short and removes the effects of leverage from the high BM long. The third is 130% long the High BM decile and has no short, to remove the effect of the short and highlight the effect of the leverage. How would those strategies have fared over the last 10 years, which, as we saw yesterday, was a period of attenuated returns for equities?

The 130/30 HML strategy

“Low BM” is the lowest BM decile, marked in red. This is Decile 1 from the Average monthly returns to decile BM portfolios chart in yesterday’s post. “High BM” is the highest BM decile, marked in blue. This is Decile 1o from yesterday’s chart. The green “HML” line is -30% of the return on the low BM decile and 130% the return on the high BM decile. “Delta,” in purple, is the difference between the return on the low and high BM components of the HML strategy. Here’s the chart:

Several observations can be made about the chart. First, as at September 2009, the “Low BM” strategy (in red) is down 3%, which approximates the return on the market as a whole over the last 10 years. The “High BM” strategy (in blue) is up about 95%, which is not a great return over 10 years, but well ahead of the market in general and the Low BM portfolio. The HML portfolio is up around 124%, well ahead of both the Low BM and High BM portfolios. As recently as 2003, the 130/30 HML portfolio was underwater and it nearly accomplished this feat again in 2009. It performed almost in line with the High BM portfolio while the market was tanking, which is when I would have expected the Low BM short to protect the return on the HML, affording only a little protection. It seems that the return on the 130% long component of the High BM portfolio caused the HML strategy to tank with the High BM portfolio, although it also caused it to recover much faster. While GSAM is correct that HML returns have been reduced since late 2007, the 10-year return on the 130/30 HML is attractive.

The 100/100 HML strategy

In this chart the green “HML” line is -100% of the return on the low BM decile and 100% the return on the high BM decile:

The 100/100 HML chart illustrates several points. First, as at September 2009, the HML portfolio is up 98%, in line with the High BM portfolio. Where the Low BM portfolio falls, the 100% low BM short in the HML protects it. For the last 10 years, it has generally protected the HML return. Of course it hurts the HML’s performance when the low BM short rises, which, as we saw yesterday, has generally been the case since 1926.

The Levered High BM strategy

This chart shows the High BM portfolio levered at the same rate as the 130/30 HML strategy (i.e.130%), but without the low BM short:

The Levered High BM portfolio tracks (visually) almost identically to the 130/30 HML strategy (Levered High BM closed up 123%, HML closed up 124%). This suggests to me that most of the additional gains in the 130/30 HML strategy over the High BM strategy are simply attributable to the leverage in the HML, and not out of any protection afforded by the low BM short.

Recent returns to HML depressed

A casual perusal of any chart above illustrates that HML has not progressed since August 2007. GSAM argues that this is a secular phenomenon due to overcrowding. I’m not convinced that it’s a secular phenomenon, but it’s certainly noteworthy. I’m also not entirely convinced that it’s due to overcrowding. It could just as easily be a function of the high price for equities in August 2007 and again now. GSAM argues that your view on the phenomenon as being either cyclical or secular is key to how you position yourself for the future. If you believe it’s cyclical, you’re a “Sticker,” and, if you believe it’s secular, you’re an “Adapter”. The distinction, according to Zero Hedge, is as follows:

The Stickers believe this is part of the normal volatility of such strategies

• Long-term perspective: results for HML (High Book-to-Price Minus Low Book-to-Price) and WML (Winners Minus Losers) not outside historical experience

• Investors who stick to their process will end up amply rewarded

The Adapters believe that quant crowding has fundamentally changed the nature of these factors

• Likely to be more volatile and offer lower returns going forward

• Need to adapt your process if you want to add value consistently in the future

I’m in the cyclical camp, but it may be other players withdrawing from the field that causes the cycle to turn.

Conclusion

Despite GSAM’s protestations to the contrary, and despite the diminution of equity returns at both the value and glamour ends of the market, HML remains an attractive strategy. Over a 10-year period of attenuated equity returns, a 130/30 HML strategy performed very well. It seems, however, that most of the returns to the 130/30 strategy are attributable to the leverage in the high BM portfolio, rather than any protection in the low BM short. As we saw yesterday, the low BM decile, while generating a lower return than the high BM decile over time, has mainly generated positive returns. This means that the low BM short will generally hurt the HML strategy’s performance.

My vast preference remains a leverage-free, long-only, ultra-high BM portfolio for a variety of reasons not connected with the chronic underperformance of the short, most notably that it’s the lowest risk portfolio available (despite what Fama and French say). In my opinion, the diminution in returns to the high BM strategy we observed yesterday is a cyclical phenomenon. 25 years is a long time for a cycle to turn, but I’m reasonably confident that the high BM strategy will again generate average monthly returns in line with the long-run average on yesterday’s chart, which means average monthly returns in the vicinity of 1.2% to 1.4%. I don’t foresee this occurring any time soon, and I think dwindling returns are the order of the day for the next 5 or 10 years. If I had to be anywhere in equities, however, I’d start in the cheapest decile of the market on a price-to-book basis and work my way through to those with the highest proportion of current assets. That’s a proven strategy that served Graham and Schloss very well, and, as far as I can see, there’s no reason why it shouldn’t continue to work.

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Recently we discussed a Goldman Sachs Asset Management (GSAM) presentation, Maybe it really is different this time, in which GSAM argued that High Minus Low or HML, a quantitative investment strategy that seeks to profit from the performance differential between high and low book value-to-market value (BM) stocks, had underperformed since August 2007 due to “overcrowding.” Robert Litterman, Goldman Sachs’ Head of Quantitative Resources, was quoted as saying that “strategies such as those which focus on price rises in cheaply-valued stocks…[have] become very crowded” since August 2007 and therefore unprofitable. The GSAM presentation included a variety of slides showing the reduction in the returns to HML and the growth in the number of practitioners in the space (See my summary of the GSAM presentation and Zero Hedge’s take on it).

Over the last week I’ve run my own informal analysis of the returns to HML and its components, high and low BM stocks. The resulting post has metastasised into an epic (by my standards), so I’ve broken it into two parts, Component returns (Part 1) and HML returns (Part 2). In today’s post, Component returns, I describe the HML strategy in some detail and analyse the long-term diminution in the returns to the components of HML, namely, high BM (low P/B) stocks and low BM (high P/B) stocks. The results are stunning. The returns to the high BM and low BM stocks have been attenuating significantly over time. Further, the phenomenon persists over whichever recent period we elect to choose (since 1926, or the last 25 years, 20 years, 15 years or 10 years). This suggests that it’s got harder over time to earn excess returns as a value investor employing a high BM strategy.

In tomorrow’s post, I analyse the returns to HML at the strategy level and ask whether the returns to HML are really just returns to a levered high BM strategy. In summary, the returns to HML have indeed stagnated since late 2007. I’m not sure that this is attributable to “overcrowding” as GSAM suggests or just a function of the underlying market performance of the components of HML (i.e. everything has been and continues to be expensive, leaving little room for good returns). Interestingly, GSAM’s argument that HML is dead as of August 2007 aside, the HML strategy has performed reasonably well over the last 10 years, which has been a period of diminished (or non-existent) returns for equities. The returns to a 130/30 HML strategy over the last 10 years significantly outpace a high BM strategy and the market in general. Surprisingly (to me at least), the low BM short didn’t add much to HML returns. This is especially surprising give that the period analysed was one where the low BM stocks bore the brunt of the collapse. That observation requires some further analysis, but it’s a prima facie argument that most of the returns to HML are due to the leverage inherent in the strategy.

A primer on HML

As I mentioned above, HML is a quantitative investment strategy that seeks to profit from the performance differential between high and low book value-to-market value stocks. It’s interesting to me because it appears to be a value-based strategy. In actuality, it finds its roots in the Fama and French Three-Factor Model, which is an attempt to explain the excess returns attributable to value stocks within an efficient markets context. HML also owes an intellectual debt to the various studies demonstrating the relative outperformance of low price-to-value stocks over higher price-to-value stocks – Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986), Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987), Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s Contrarian Investment, Extrapolation and Risk (1994) as updated by The Brandes Institute’s Value vs Glamour: A Global Phenomenon (2008) – but it is most closely associated with Fama and French.

Fama and French observed in their 1992 paper, The Cross-Section of Expected Stock Returns, that there is “striking evidence” of a “strong positive relation between average return and book-to-market equity” [“BE” is book equity and “ME” is market equity, so “BE/ME” is just BM, the inverse of P/B]:

Average returns rise from 0.30% for the lowest BE/ME portfolio to 1.83% for the highest, a difference of 1.53% per month.

Note also that the strong relation between book-to-market equity and average return is unlikely to be a [beta] effect in disguise.*

[Although] BE/ME has long been touted as a measure of the return prospects of stocks, there is no evidence that its explanatory power deteriorates through time. The 1963-1990 relation between BE/ME and average return is strong, and remarkably similar for the 1963-1976 and 1977-1990 subperiods. Second, our preliminary work on economic fundamentals suggests that high-BE/ME firms tend to be persistently poor earners relative to low-BE/ME firms.

Ibbotson (1986), DeBondt and Thaler (1987), Lakonishok, Shleifer, and Vishny (1994) and The Brandes Institute (2008) all make similar findings. Low P/B stocks outperform higher P/B stocks in the aggregate, and in rank order, the cheapest decile, quintile, quartile etc outperforming the next cheapest and so on. This phenomenon obviously presents a problem for the efficient markets crowd because the historic excess returns of value stocks over glamour stocks cannot be explained by the traditional CAPM model. Fama and French’s solution is the Three-Factor Model.

Fama and French attribute the variation in average returns between high and low BM stocks to “relative distress,” arguing that value strategies (i.e. high BM stocks) produce abnormal returns because they are fundamentally riskier. This observation is the impetus for the inclusion of “value” as a factor in Fama and French’s Three-Factor Model, where it is accounted for as “HML”. (It was also the impetus for Piotroski’s F_SCORE, which seeks to use “context-specific financial performance measures to differentiate strong and weak firms” within the universe of high BM stocks.)

HML in the Fama and French context measures the historic excess returns of value stocks over growth stocks, which break the traditional CAPM model. Here’s how they circumvent the problem: By splitting out HML from the market return and labelling the portion of the excess return attributable to HML as “riskier,” Fama and French can explain away those excess returns. They then simply apply an HML coefficient to a portfolio of value stocks and – abracadabra – the expected return is higher than the market return but explainable within the efficient markets world because of the additional risk attributable to value. The proponents of the efficient markets hypothesis breathe a sigh of relief and continue to believe that no one can make excess returns once those returns are adjusted for risk. (Don’t mention that value is not, per se, riskier, because such an observation would break the model all over again.) It’s worth noting that Lakonishok, Shleifer, and Vishny (1994) disagree with Fama and French’s assertion that the returns are due to financial distress, arguing instead that the returns to value are the result of a bias that leads investors to extrapolate past performance too far into the future, not fully appreciating the phenomenon of mean reversion.

Whatever the basis for the returns to value, the phenomenon has attracted a substantial following in the world of quantitative investing. So much so that GSAM thinks the field is now “overcrowded” and that explains the diminution in returns since August 2007. The attraction of the HML strategy to a quant is easy to understand: They’re agnostic to the reason for the excess returns, and more than happy to earn some and remain market neutral. The solution is to split out from the market return the excess return attributable to HML. How does one do that in practice? One simply buys the value stocks and sells the glamour stocks. This means buying high BM stocks and selling short low BM stocks. It’s extraordinary that, despite the tortured EMH reasoning, HML is a strategy that a value investor would recognize and (shorting, leverage and aggregation notwithstanding) probably approve of in its general terms. Before looking at the returns to the HML strategy, I think it’s useful to look under the hood and consider the “engine” of the strategy, which is the returns to the underlying components.

* One of the observations made by Fama and French (1992) is that “average returns for negative BE firms are high, like the average returns of high BE/ME firms. Negative BE (which results from persistently negative earnings) and high BE/ME (which typically means that stock prices have fallen) are both signals of poor earnings prospects.” This is very interesting. I’ve never heard of a negative BM strategy. While it makes me a little nervous to think about, it’s possible that negative BM stocks are an untapped source of returns. Perhaps it’s just the leverage at the company level, but it warrants a further investigation and a later post. Let me know if you’ve got any data or studies on the subject.

Returns to HML’s components: High BM and Low BM

There are two components to the HML strategy: The high BM long and the low BM short. The strategy seeks to remain market neutral by selling short low BM stocks, which are expected to fall back to the mean market BM value, and using leverage to buy high BM stocks, which are expected to rise to the mean market BM value. To see how each component performs, I’ve produced a chart of average monthly stock returns since 1926. Before I present the graph, a quick disclaimer: What follows does not amount to a formal academic study into the relative performance of high BM and low BM over time. It’s nothing more than me messing around with COMPUSTAT return data and plugging it into an Excel spreadsheet. Stocks were divided into ten deciles based on book value-to-market value. Average returns for each decile were calculated on a monthly basis over five different time periods:

  • “All” from July 1926 to September 2009
  • “25 Years”, from October 1984 to September 2009
  • “20 Years”, from October 1989 to September 2009
  • “15 Years”, from October 1994 to September 2009
  • “10 Years”, from October 1999 to September 2009

“Decile 10” is formed from the portfolio of stocks with the highest BM ratio (lowest P/B), “Decile 1” is the portfolio of stocks with the lowest BM ratio (highest P/B) and so on. Here’s the chart:

For me, three observations leap out from the chart. First, the relationship between high and low BM deciles is relatively unchanged over time. The relatively high BM stocks in deciles 10, 9, and 8 tend to outperform the relatively lower BM stocks in deciles 1, 2, and 3. With few exceptions, the higher the ratio of book to market, the better the performance.

Second, the returns to all deciles have attenuated significantly over time. This was one of the questions I had after reading The Brandes Institute’s Value vs Glamour: A Global Phenomenon update of the Contrarian Investment, Extrapolation and Risk. The Brandes Institute paper didn’t split out from Lakonishok, Shleifer, and Vishny’s paper the more recent returns to high BM stocks, but the blended return was lower in the later study, suggesting that returns had diminished in the intervening period. As far as this simple analysis goes, it seems to confirm that impression.

The third observation is that the low BM decile – Decile 1 – has for most of the time had a positive monthly return. It is only over the last 10 years that the monthly returns for the lowest BM decile have been negative. This is significant because this means that, the last 10 years aside, one employing the HML strategy would have lost money on the low BM short, and would have earned better returns without the short. Over the last 10 years, however, one would expect that the low BM short has paid off handsomely. As you’ll see tomorrow, this is not actually the case.

Hat tip to the Ox for the return data.

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The phenomenal Zero Hedge has an article, Goldman Claims Momentum And Value Quant Strategies Now Overcrowded, Future Returns Negligible, discussing Goldman Sachs head of quantitative resources Robert Litterman’s view that  “strategies such as those which focus on price rises in cheaply-valued stocks…[have] become very crowded” since August 2007 and therefore unprofitable. The strategy to which Litterman refers is “HML” or “High Book-to-Price Minus Low Book-to-Price,” which is particularly interesting given our recent consideration of the merits of price-to-book value as an investment strategy and the various methods discussed in the academic literature for improving returns from a low P/B strategy. Litterman argues that only special situations and event-driven strategies that focus on mergers or restructuring provide opportunities for profit:

What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.

In a follow-up article, More On The Futility Of Groupthink Quant Strategies, And Why Momos Are Guaranteed To Lose Money Over Time, Zero Hedge provides a link to a Goldman Sachs Asset Management presentation, Maybe it really is different this time (.pdf via Zero Hedge), from the June 2009 Nomura Quantitative Investment Strategies Conference. The presentation supports Litterman’s view on the underperformance of HML since August 2007. Here’s the US:

Here’s a slide showing the ‘overcrowding” to which Litterman refers:

And its effect on the relative performance of large capitalization value to the full universe:

The returns get really ugly when transaction costs are factored into the equation:

A factor decay graph showing the decline in legacy portfolios relative to current portfolios, lower means and faster decay indicating crowding:

Goldman says that there are two possible responses to the underperformance, and characterizes each as either a “sticker” or an “adapter.” The distinction, according to Zero Hedge, is as follows:

The Stickers believe this is part of the normal volatility of such strategies

• Long-term perspective: results for HML (High Book-to-Price Minus Low Book-to-Price) and WML (Winners Minus Losers) not outside historical experience

• Investors who stick to their process will end up amply rewarded

The Adapters believe that quant crowding has fundamentally changed the nature of these factors

• Likely to be more volatile and offer lower returns going forward

• Need to adapt your process if you want to add value consistently in the future

In Contrarian Investment, Extrapolation, and Risk, Josef Lakonishok, Andrei Shleifer, and Robert Vishny argued that value strategies produce superior returns because most investors don’t fully appreciate the phenomenon of mean reversion, which leads them to extrapolate past performance too far into the future. Value strategies “exploit the suboptimal behavior of the typical investor” by behaving in a contrarian manner: selling stocks with high past growth as well as high expected future growth and buying stocks with low past growth and as well as low expected future growth. It makes sense that crowding would reduce the returns to a contrarian strategy. Lending further credence to Litterman and Goldman’s argument is the fact that the underperformance seems to be most pronounced in the large capitalization universe (see the “A closer look – value” slide) where the larger investors must fish. If you’re not forced by the size of your portfolio to invest in that universe it certainly makes sense to invest where contrarian returns are still available. Special situations like liquidations and event-driven investments like activist campaigns offer a place to hide if (and when) the market resumes the long bear.

My firm Acquirers Funds® helps you put the acquirer’s multiple into action. Click here to learn more about our deep value strategy.

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The second method for boosting the performance of book value as a predictor of future investment returns is Joseph D. Piotroski’s elegant F_SCORE. Piotroski first discussed his F_SCORE in 2002 in Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. In the paper, Piotroski examines whether the application of a simple accounting-based fundamental analysis strategy to a broad portfolio of high book-to-market firms can improve the returns earned by an investor. Piotroski found that his method increased the mean return earned by a low price-to-book investor “by at least 7 1/2% annually” through the “selection of financially strong high BM firms.”

In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23% annual return between 1976 and 1996, and the strategy appears to be robust across time and to controls for alternative investment strategies.

With a return of that magnitude, it’s well worth a deeper look.

Piotroski’s rationale

Piotroski uses “context-specific financial performance measures to differentiate strong and weak firms:”

Instead of examining the relationships between future returns and particular financial signals, I aggregate the information contained in an array of performance measures and form portfolios on the basis of a firm’s overall signal. By focusing on value firms, the benefits to financial statement analysis (1) are investigated in an environment where historical financial reports represent both the best and most relevant source of information about the firm’s financial condition and (2) are maximized through the selection of relevant financial measures given the underlying economic characteristics of these high BM firms.

F_SCORE

On the assumption that the “average high BM firm is financially distressed,” Piotroski chose nine fundamental signals to measure three areas of the firm’s financial condition: profitability, financial leverage/liquidity, and operating efficiency:

In this paper, I classify each firm’s signal realization as either “good” or “bad,” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). I define the aggregate signal measure, F_SCORE, as the sum of the nine binary signals. The aggregate signal is designed to measure the overall quality, or strength, of the firm’s financial position, and the decision to purchase is ultimately based on the strength of the aggregate signal.

F_SCORE component: Profitability

On the basis that “current profitability and cash flow realizations provide information about the firm’s ability to generate funds internally,” Piotroski uses four variables to measure these performance-related factors: ROA, CFO, [Delta]ROA, and ACCRUAL:

I define ROA and CFO as net income before extraordinary items and cash flow from operations, respectively, scaled by beginning of the year total assets. If the firm’s ROA (CFO) is positive, I define the indicator variable F_ROA (F_CFO) equal to one, zero otherwise. I define ROA as the current year’s ROA less the prior year’s ROA. If [Delta]ROA [is greater than] 0, the indicator variable F_[Delta]ROA equals one, zero otherwise.

I define the variable ACCRUAL as current year’s net income before extraordinary items less cash flow from operations, scaled by beginning of the year total assets. The indicator variable F_ ACCRUAL equals one if CFO [is greater than] ROA, zero otherwise.

F_SCORE component: Leverage, liquidity, and source of funds

For the reason that “most high BM firms are financially constrained” Piotroski assumes that an increase in leverage, a deterioration of liquidity, or the use of external financing is a bad signal about financial risk. Three of the nine financial signals are therefore designed to measure changes in capital structure and the firm’s ability to meet future debt service obligations: [Delta]LEVER, [Delta]LIQUID, and EQ_OFFER:

[Delta]LEVER seeks to capture changes in the firm’s long-term debt levels:

I measure [Delta]LEVER as the historical change in the ratio of total long-term debt to average total assets, and view an increase (decrease) in financial leverage as a negative (positive) signal. By raising external capital, a financially distressed firm is signaling its inability to generate sufficient internal funds (e.g., Myers and Majluf 1984, Miller and Rock 1985). In addition, an increase in long-term debt is likely to place additional constraints on the firm’s financial flexibility. I define the indicator variable F_LEVER to equal one (zero) if the firm’s leverage ratio fell (rose) in the year preceding portfolio formation.

[Delta]LIQUID seeks to measure the historical change in the firm’s current ratio between the current and prior year, where Piotroski defines the current ratio as the ratio of current assets to current liabilities at fiscal year-end:

I assume that an improvement in liquidity (i.e., [Delta]LIQUID [is greater than] 0) is a good signal about the firm’s ability to service current debt obligations. The indicator variable F_[Delta]LIQUID equals one if the firm’s liquidity improved, zero otherwise.

Piotroski argues that financially distressed firms raising external capital “could be signaling their inability to generate sufficient internal funds to service future obligations” and  the fact that these firms are willing to issue equity when their stock prices are depressed “highlights the poor financial condition facing these firms.” EQ_OFFER captures whether a firm has issued equity in the year preceding portfolio formation. It is set to one if the firm did not issue common equity in the year preceding portfolio formation, zero otherwise.

F_SCORE component: Operating efficiency

Piotroski’s two remaining signals seek to measure “changes in the efficiency of the firm’s operations:” [Delta]MARGIN and [Delta]TURN. Piotroski believes these ratios are important because they “reflect two key constructs underlying a decomposition of return on assets.”

Piotroski defines [Delta]MARGIN as the firm’s current gross margin ratio (gross margin scaled by total sales) less the prior year’s gross margin ratio:

An improvement in margins signifies a potential improvement in factor costs, a reduction in inventory costs, or a rise in the price of the firm’s product. The indicator variable F_[Delta]MARGIN equals one if [Delta]MARGIN is positive, zero otherwise.

Piotroski defines [Delta]TURN as the firm’s current year asset turnover ratio (total sales scaled by beginning of the year total assets) less the prior year’s asset turnover ratio:

An improvement in asset turnover signifies greater productivity from the asset base. Such an improvement can arise from more efficient operations (fewer assets generating the same levels of sales) or an increase in sales (which could also signify improved market conditions for the firm’s products). The indicator variable F_[Delta]TURN equals one if [Delta]TURN is positive, zero otherwise.

F_SCORE formula and interpretation

Piotroski defines F_SCORE as the sum of the individual binary signals, or

F_SCORE = F_ROA + F_[Delta]ROA + F_CFO + F_ ACCRUAL + F_[Delta]MARGIN + F_[Delta]TURN + F_[Delta]LEVER + F_[Delta]LIQUID + EQ_OFFER.

An F_SCORE ranges from a low of 0 to a high of 9, where a low (high) F_SCORE represents a firm with very few (mostly) good signals. To the extent current fundamentals predict future fundamentals, I expect F_SCORE to be positively associated with changes in future firm performance and stock returns. Piotroski’s investment strategy is to select firms with high F_SCORE signals.

Piotroski’s methodology

Piotroski identified firms with sufficient stock price and book value data on COMPUSTAT each year between 1976 and 1996. For each firm, he calculated the market value of equity and BM ratio at fiscal year-end. Each fiscal year (i.e., financial report year), he ranked all firms with sufficient data to identify book-to-market quintile and size tercile cutoffs and classified them into BM quintiles. Piotroski’s final sample size was 14,043 high BM firms across the 21 years.

He measured firm-specific returns as one-year (two-year) buy-and-hold returns earned from the beginning of the fifth month after the firm’s fiscal year-end through the earliest subsequent date: one year (two years) after return compounding began or the last day of CRSP traded returns. If a firm delisted, he assumed the delisting return is zero. He defined market-adjusted returns as the buy-and-hold return less the value-weighted market return over the corresponding time period.

Descriptive evidence of high book-to-market firms

Piotroski provides descriptive statistics about the financial characteristics of the high book-to-market portfolio of firms, as well as evidence on the long-run returns from such a portfolio. The average (median) firm in the highest book-to-market quintile of all firms has a mean (median) BM ratio of 2.444 (1.721) and an end-of-year market capitalization of 188.50(14.37)M dollars. Consistent with the evidence presented in Fama and French (1995), the portfolio of high BM firms consists of poor performing firms; the average (median) ROA realization is –0.0054 (0.0128), and the average and median firm saw declines in both ROA (–0.0096 and –0.0047, respectively) and gross margin (–0.0324 and –0.0034, respectively) over the last year. Finally, the average high BM firm saw an increase in leverage and a decrease in liquidity over the prior year.

High BM returns

The table below (Panel B of Table 1) extracted from the paper presents the one-year and two-year buy-and-hold returns for the complete portfolio of high BM firms, along with the percentage of firms in the portfolio with positive raw and market-adjusted returns over the respective investment period. Consistent with the findings in the Fama and French (1992) and Lakonishok, Shleifer, and Vishny (1994) studies, high BM firms earn positive market-adjusted returns in the one-year and two-year periods following portfolio formation:

Perhaps Piotroski’s most interesting finding is that, despite the strong mean performance of this portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. Piotroski concludes, therefore, that any strategy that can eliminate the left tail of the return distribution (i.e., the negative return observations) will greatly improve the portfolio’s mean return performance.

High BM and F_SCORE returns

Panel A of Table 3 below shows the one-year market-adjusted returns to the Piotroski F_SCORE strategy:

The table demonstrates the return difference between the portfolio of high F_SCORE firms and the complete portfolio of high BM firms. High F_SCORE firms earn a mean market-adjusted return of 0.134 versus 0.059 for the entire BM quintile. The return improvements also extend beyond the mean performance of the various portfolios. The results in the table shows that the 10th percentile, 25th percentile, median, 75th percentile, and 90th percentile returns of the high F_SCORE portfolio are significantly higher than the corresponding returns of both the low F_SCORE portfolio and the complete high BM quintile portfolio using bootstrap techniques. Similarly, the proportion of winners in the high F_SCORE portfolio, 50.0%, is significantly higher than the two benchmark portfolios (43.7% and 31.8%). Overall, it is clear that F_SCORE discriminates between eventual winners and losers.

Conclusion

Piotroski’s F_SCORE is clearly a very useful metric for high BM investors. Piotroski’s key insight is that, despite the strong mean performance of a high BM portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. The F_SCORE is designed to eliminate the left tail of the return distribution (i.e., the negative return observations). It succeeds in doing so, and the resulting returns to high BM and high F_SCORE portfolios are nothing short of stunning.

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This week we’ve been examining the various studies that have considered book value as a predictor of future investment returns, and methods for “juicing” or improving its performance. Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s landmark 1994 study Contrarian Investment, Extrapolation, and Risk examined book value in the context of a larger investigation into the performance of value stocks relative to glamour stocks in the United States. Book value was one of four one-variable metrics used to classify a stock as “value” or “glamour” (the others were cash flow, earnings and 5-year average growth rate of sales). Lakonishok, Shleifer, and Vishny (LSV) argue that value strategies produce superior returns because most investors don’t fully appreciate the phenomenon of mean reversion, which leads them to extrapolate past performance too far into the future. To exploit the flaw in intuitive forecasts – you know how I love a counter-intuitive strategy – they argue that contrarian investors should sell stocks with high past growth as well as high expected future growth and buy stocks with low past growth and as well as low expected future growth. In practice, this means adding to each of the four one-variable value metrics a second dimension to further tune the selection process. The result is LSV’s Two-Dimensional Classification.

Contrarian Investment, Extrapolation, and Risk

In Contrarian Investment, Extrapolation, and Risk LSV define “value strategies” as “buying stocks that have low prices relative to earnings, dividends, book assets, or other measures of fundamental value.” They argue that, while there is some agreement that value strategies produce higher returns, the interpretation of why they do so is more controversial. The paper is a response to Fama and French’s 1992 paper, The Cross-Section of Expected Stock Returns, which argued that value strategies produce abnormal returns only because they are fundamentally riskier. LSV seek to demonstrate that value strategies yield higher returns because these strategies “exploit the suboptimal behavior of the typical investor” and “not because these strategies are fundamentally riskier.”  (LSV’s research was updated this year by The Brandes Institute, who extended LSV’s research through to June 2008, creating a 40-year comparison of the relative performance of value and glamour stocks.)

LSV test two potential explanations for the outperformance of value stocks over glamour stocks:

  1. LSV’s contrarian model, which argues that value strategies produce superior returns because investors extrapolate past performance too far into the future.
  2. Fama and French’s contention that value stocks are fundamentally riskier than glamour stocks. This second potential explanation is outside the scope of this post, but is dealt with in some detail in the paper. I encourage you to read it if you’re interested in the efficient markets debate.

LSV test the contention that value strategies produce superior returns because investors extrapolate past performance too far into the future by examining simple one-variable classifications of glamour and value stocks. Glamour stocks are those that “have performed well in the past,” and “are expected by the market to perform well in the future.” Value stocks are those that “have performed poorly in the past and are expected to continue to perform poorly.” The stocks are classified on the basis of one of four variables: book-to-market (B/M, the inverse of price-to-book), cash flow-to-price (C/P), earnings-to-price (E/P), and 5-year average growth rate of sales (GS). LSV examine 2,700 firms on the NYSE and AMEX between 1968 and 1989. At the end of each April, they rank each stock on the basis of the variable tested (B/M, C/P etc) and then divide the stocks into deciles. Each decile is treated as a portfolio and held for 5 years. LSV track the performance of each decile portfolio in each of the 5 years and present the results as follows (Rt is the average return in year t over the 5 years after formation, CR5 is the compounded 5-year return assuming annual rebalancing. SAAR is the average annual size-adjusted return computed over the 5 years after formation. The Glamour portfolio is the decile portfolio containing stocks ranked lowest on B/M, C/P, or E/P, or highest of GS and vice versa for the Value portfolio):

As the four panels make clear, value outperforms glamour in rank order and regardless of the simple one-variable classification chosen. LSV attribute the outperformance to the failure of investors to formulate their predictions of the future without a “full appreciation of mean reversion.”

That is, individuals tend to base their expectations on past data for the individual case they are considering without properly weighting data on what psychologists call the “base rate,” or the class average. Kahneman and Tversky (1982, p. 417) explain:

One of the basic principles of statistical prediction, which is also one of the least intuitive, is that extremeness of predictions must be moderated by considerations of predictability… Predictions are allowed to match impressions only in the case of perfect predictability. In intermediate situations, which are of course the most common, the prediction should be regressive; that is, it should fall between the class average and the value that best represents one’s impression of the case at hand. The lower the predictability the closer the prediction should be to the class average. Intuitive predictions are typically nonregressive: people often make extreme prediction on the basis of information whose reliability and predictive validity are known to be low.

Anatomy of a Contrarian Strategy: LSV’s Two-Dimensional Classification

According to LSV, to exploit this flaw of intuitive forecasts, contrarian investors should sell stocks with high past growth as well as high expected future growth and buy stocks with low past growth and as well as low expected future growth.

Prices of these stocks are likely to reflect the failure of investors to impose mean reversion on growth forecasts.

LSV test the Two-Dimensional Classifications in a similar manner to the one-variable classifications above. At the end of each April between 1968 and 1989, 9 portfolios of stocks are formed. The stocks are independently sorted into ascending order in 3 groups (rather than deciles, for the obvious reason – 9 annual portfolios is easier to track than 100): 1. the bottom 30%, 2. the middle 40%, and 3. the top 30% based on each of two variables. The sorts are for 5 pairs of variables: C/P and GS, B/M and GS, E/P and GS, E/P and  B/M and B/M and C/P. Depending on the two variables used for classification, the Value portfolio either refers to the portfolio containing stocks ranked in the top group (3.) on both variables from among C/P, E/P, or B/M, or else the portfolio containing stocks ranking in the top group on one of those variables and in the bottom group (1.) on GS and vice versa for Glamour. (For the purposes of this post, I’m including only those examining B/M as one of the variables. The others are, however, well worth considering. Value determined on the basis C/P or E/P combined with GS produced slightly higher cumulative returns averaged across all firms for the period of the study. Interestingly, this phenomenon reversed in large stocks, with B/M-based strategies producing slightly higher cumulative returns in large stocks.):

These tables demonstrate that, within the set of firms whose B/M ratios are the highest (in other words, the lowest price-to-book value), further sorting on the basis of another value variable – whether it be C/P, E/P or low GS – can enhance returns. This is LSV’s Two-Dimensional Classification. LSV conclude that value strategies based jointly on past performance and expected future performance produce higher returns than “more ad hoc strategies such as that based exclusively on the B/M ratio.” The strategy is quite useful. It can be applied to large stocks, which means that it can be used to implement trading strategies for larger and institutional investors, and will continue to generate superior returns.

Next we examine Joseph D. Piotroski’s F_SCORE as a means for juicing P/B.

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Price-to-book value is demonstrably useful as a predictor of future investment returns. As we discussed yesterday in Testing the performance of price-to-book value, various studies, including Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986), Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987), Josef Lakonishok, Andrei Shleifer, and Robert Vishny Contrarian Investment, Extrapolation and Risk (1994) and The Brandes Institute’s Value vs Glamour: A Global Phenomenon (2008) all conclude that lower price-to-book value stocks tend to outperform higher price-to-book value stocks, and at lower risk. Understanding this to be the case, the obvious question for me becomes, “Within the low price-to-book value universe, is there any way of further distinguishing likely stars from likely laggards and thereby further increasing returns?” The answer can be found in two studies: Joseph D. Piotroski’s seminal paper Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers (.pdf) and Lakonishok, Shleifer, and Vishny’s original Contrarian Investment, Extrapolation and Risk (1994).

I’ll be discussing both of these studies in some detail over the next two days, starting with LSV’s Two-Dimensional Classifications tomorrow.

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